Optimal time-dependent levels of weight-bearing for bone fracture healing under Ilizarov circular fixators

https://doi.org/10.1016/j.jmbbm.2021.104611Get rights and content

Highlights

  • An approach to find optimal weight-bearing levels for fracture healing is presented.

  • Fracture site revascularization and bone resorption processes govern these levels.

  • Partial weight-bearing is recommended for Ilizarov fixators for longer periods.

  • Fixator configuration affects weight-bearing only in the early stages of healing.

  • Use of half pins is preferable over pretensioned wires in Ilizarov fixators.

Abstract

It is known that weight-bearing exercises under Ilizarov circular fixators (ICF) could enhance bone fracture healing by mechano-regulation. However, interfragmentary movements at the fracture site induced by weight-bearing may inhibit angiogenesis and ultimately delay the healing process. To tackle this challenge, a computational model is presented in this study which considers the spatial and temporal changes in mechanical properties of fracture callus to predict optimal levels of weight-bearing during fracture healing under ICF. The study takes sheep fractures as example and shows that the developed model has the capability of predicting patient specific, time-dependent optimal levels of weight-bearing which enhances mechano-regulation mediated healing without hindering the angiogenesis process. The results demonstrate that allowable level of weight-bearing and timings depend on fracture gap size. For normal body weights (BW) and moderate fracture gap sizes (e.g. 3 mm), weight-bearing with 30% BW could start by week 4 post-operation and gradually increase to 100% BW by week 11. In contrast, for relatively large fracture gap sizes (i.e. 6 mm), weight-bearing is recommended to commence in later stages of healing (e.g. week 11 post-operation). Furthermore, increasing ICF stiffness (e.g. using half pins instead of pretension wires) can increase the level of weight-bearing significantly in the early stages up to a certain time point (e.g. week 8 post-operation) beyond which no noticeable benefits could be achieved. The findings of this study have potential applications in designing post-operative weight bearing exercises.

Introduction

Musculoskeletal injuries make up a significant proportion of the total world health expenses (Meling et al., 2009). In particular, long bone fractures is one of the frequent non-fatal injuries reported worldwide which occurs due to various reasons such as osteoporosis, trauma etc. (Li et al., 2019; Meling et al., 2009; Zhang et al., 2017b). Although bone fractures naturally heal by themselves, its intrinsic healing capacity may not always be enough, and some external intervention may be required in some cases (Makhdom et al., 2015). It is in the hands of orthopaedic surgeons to decide what treatment strategy to choose on a case-by-case basis. This decision-making process is challenging and requires predictions to be made about the effects of a chosen strategy on fracture healing.

Bone fracture healing is a very complex process involving many cellular, molecular and tissue level events in an orchestrated manner (Ai-Aql et al., 2008). These events are regulated by many biological and mechanical factors and are controllable externally. The basis behind choosing a treatment strategy is providing the most favourable environment for healing. One of the most important requirements of successful healing is the re-establishment of vasculature within the fracture site (Claes et al., 2002, 2003). Therefore, it is essential that the angiogenesis process is not hindered within the fracture site during healing. The other prerequisite for fracture healing is mechanical stability (Claes et al., 2002). It is known that unstable fractures end up in delayed unions or non-unions. One of the reasons behind this phenomenon is that unstable fracture microenvironments can affect both angiogenesis and synthesis of bone tissue (Claes et al., 2002; Miramini et al., 2018). If fracture site is rigidly fixed, fractures tend to heal by primary healing where bone tends to re-establish itself from the existing bone fragments (Claes et al., 1997; Marsell and Einhorn, 2011; Miramini et al., 2016b). However, this is not the common form of fracture healing (Marsell and Einhorn, 2011).

Under flexible fixations, most fractures heal by secondary fracture healing, where the healing process goes through three distinct but overlapping phases (Claes et al., 2012; Marsell and Einhorn, 2011). Secondary healing begins with the ‘inflammation phase’. Soon after fracture, due to rupture of blood vessels, blood flows into the fracture site to form haematoma which is soon followed by inflammatory response (Ghiasi et al., 2017). Haematoma is subsequently replaced with very soft fibrous granulation tissue which forms the initial fracture callus. This acts as a temporary scaffold for the cascade of cellular activities in the subsequent phases (Ghiasi et al., 2017).

Early callus provides an excellent environment for multipotent mesenchymal stem cells (MSCs) to migrate within the fracture site and differentiate into tissue forming cells like osteoblasts, chondrocytes and fibroblasts (Ai-Aql et al., 2008). This differentiation process is regulated by the local mechanical and biological microenvironments and thus spatially varying within the callus. Osteoblasts are formed to synthesise woven bone tissue via intramembranous ossification at relatively low strain environments close to the periosteum and far from the fracture gap (Claes et al., 2012). Within the unstable fracture gap, MSCs predominantly differentiate into fibroblasts to synthesis fibrous connective tissue. In other regions MSCs differentiate into chondrocytes to synthesis cartilage tissue (Claes et al., 2012). Bone forming front gradually moves towards the fracture gap as healing progresses, while cartilage stabilizes the areas surrounding the fracture gap (Claes and Heigele, 1999; Claes et al., 2012). Subsequently, cartilage gets transformed into woven bone tissue by endochondral ossification as the microenvironment becomes favourable for osteogenesis. These changes lead to stiffening of external callus and minimization of interfragmentary movement (IFM) within the fracture gap. As the fracture gap becomes increasingly stable, fibrous tissue gets replaced by woven bone tissue via intramembranous ossification (Claes et al., 2012). In summary, during this phase, fracture is repaired by transforming the initial soft granulation tissue filled callus entirely into woven bone via different pathways and hence called the reparative phase.

In the next phase, woven bone is fully replaced by well organised lamellar bone by a process known as remodelling. This process may take several months to years after the fracture is fully bridged by woven bone. During the remodelling phase, bone regains its pre-fracture geometry and strength (Doblaré et al., 2004; Zhang et al., 2012). In clinical point of view, no further treatment is generally necessary after the end of reparative phase. The fracture is deemed to have healed if clinical union of bone fragments is detected (Hak et al., 2014). Therefore, the objective of surgeons is to shorten the reparative phase and achieve clinical union within accepted timeframes. This requires maintenance of conducive environments within the fracture site as much as possible. Experimental evidence suggests that, level of vascularity and local mechanical stability play vital roles in regulating tissue synthesis in the reparative phase (Claes et al., 2002, Claes et al., 2003). Therefore, it is essential to have deeper understanding about these factors.

Fracture healing involves enhanced metabolic activities, which requires additional supply of oxygen and other essential factors such as nutrients (Saran et al., 2014). However, due to rupture of blood vessels, enough blood supply does not prevail within the fracture site, which leads to shortage of these essential factors. Studies have revealed that angiogenesis begins from existing vasculature at a very early stage of fracture healing (Claes et al., 2012; Einhorn and Gerstenfeld, 2015). As angiogenesis progresses, the increase of blood supply increases the supply of oxygen and other essential factors and removal of metabolic wastes. Studies have reported that angiogenesis precedes osteogenesis, which suggests that enough blood supply is a prerequisite for osteogenesis (Saran et al., 2014). On the other hand, avascular environments with insufficient blood supply are conducive to chondrogenesis (Carter et al., 1998; Claes et al., 2012). Clearly, the level of blood supply is a regulator of differentiation pathway and tissue synthesis. However, the level of blood supply alone may not be enough to fully describe the differentiation process as vascularized zones of callus do not always lead to osteogenesis but also fibrogenesis (Claes et al., 2002, 2018). This indicates that there should be at least one more factor that governs the differentiation pathway.

It has been reported that bone tissues form under relatively stable environments, whereas fibrous tissue forms under relatively high strains (Claes et al., 2002). If good blood supply condition exists, mechanical stability controls whether osteogenic or fibrogenic pathway is taken during the healing process. Histological studies have shown that, bone formation begins adjacent to periosteum, but far from the fracture gap (Claes et al., 2012; Einhorn and Gerstenfeld, 2015), where the early blood vessels are observed and the tissue strains are relatively small. The progression of ossification follows revascularization and occurs adjacent to previously formed bone tissues where the tissue strains are minimal (Claes and Heigele, 1999; Claes et al., 2012; Saran et al., 2014). On the other hand, fibrous tissue begins to form within the fracture gap, which is in proximity to vasculature under relatively high tissue strains. Cartilage is formed in avascular and hypoxic regions where there is no formation of other two types of tissues (Burke and Kelly, 2012; Claes et al., 2012). Later on, with blood vessels invading the callus, cartilage is gradually replaced by woven bone (i.e. endochondral ossification) (Claes et al., 2012; Einhorn and Gerstenfeld, 2015). Therefore, fracture healing process can be described based on the local level of blood supply and tissue strain. It should also be noted that tissue strain does not only affect the differentiation pathways and tissue synthesis, but also angiogenesis. Relatively high tissue strains lead to blood vessel rupture and inhibition of angiogenesis (Claes et al., 2012). Thus, tissue strain has a direct and indirect effect on differentiation pathways.

To enhance intramembranous or endochondral ossifications during bone repair, it is essential that angiogenesis process is not hindered. This requires relatively low mechanical strains within the fracture site. However, very low strains can lead to significant resorption which leads to loss of bone mass, stiffness and strength (Mavčič and Antolič, 2012; Shanshan et al., 2019; Ulstrup, 2008). Therefore, there should be an optimal range of tissue strain that promote both angiogenesis and osteogenesis. Determining the level of weight-bearing corresponding to the optimal tissue strain range would have clinical significance as it would allow therapists to determine patient specific physiological exercises under different fixation configurations.

Many studies in the past have reported that too rigid or too flexible fixations are not very beneficial for healing and there should be an optimal range of stiffness for the ideal healing conditions (Bartnikowski et al., 2017; Claes et al., 1997; Miramini et al., 2015; Zhang et al., 2013). However, there is no clear consensus as to what this range is. Therefore, the selection of the most appropriate fixation type and configuration is often challenging. Since there is lack of systematic investigation into this problem, surgeons generally take a trial and error approach (Zhang et al., 2017a).

The problem gets more complex as stiffening of callus with time constantly modifies the load share between the fixator and callus. This makes it necessary to regulate the fixation stiffness or load to maintain the optimal level of IFM throughout the course of healing. External bone fixation devices are very beneficial in this regard as they allow adjustment of stiffness as required. In addition, external devices are often minimally invasive which makes them desirable.

Ilizarov circular fixator (ICF) is one such external bone fixation device which is minimally invasive, versatile, tailorable and possesses enormous potential to treat variety of bone defects (Ganadhiepan et al., 2019a, Ganadhiepan et al., 2019b). The modular nature of ICF allows it to be constructed in many possible configurations to suit patient specific needs. Even though ICFs are in existence for over six decades, systematic investigations on weight-bearing for the optimal healing conditions under ICF are still lacking. Therefore, the full potential of ICFs cannot be utilized yet. It is known that patient specific parameters such as fracture geometry and fixator configuration influence fracture healing (Claes et al., 1997; Ganadhiepan et al., 2019a; Miramini et al., 2016a). Therefore, the optimal weight-bearing for fracture healing need to be determined on a case-by-case basis taking patient specific parameters into account.

However, this is very challenging owing to number of patient specific factors that need to be considered and their complex interrelationships. Computational models provide an excellent framework for this problem as it can handle multiple inputs and provide reasonably accurate solutions relatively quickly. Different aspects of ICF have been studied numerically using computational models in the past (Ganadhiepan et al., 2019a, 2019b; Watson et al., 2007; Zamani and Oyadiji, 2009, Zamani and Oyadiji, 2010). However, to our best knowledge, studies on determining optimal weight-bearing for fracture healing under ICF so far are very limited.

The purpose of this study is to develop a computational framework for determining optimal weight-bearing levels for fractures treated with ICF taking patient specific factors as inputs. In this study, we considered different ICF configurations, fracture gap sizes (GS) and body weights (BW) and investigated how these important patient specific factors affect the time dependent optimal weight bearing levels for fracture healing. This study exhibits the potential of computational models in designing patient specific post-operative weight bearing exercises.

Section snippets

Materials and methods

The present study mainly focuses on the reparative phase (second phase) of fracture healing without considering the initial inflammatory responses and the transformation of woven bone into lamellar bone (remodelling) (Isaksson et al., 2006a, 2008, 2008; Lacroix et al., 2002; Miramini et al., 2014; Simon et al., 2011). In addition, it is assumed that local vascularity and tissue strain are two critical determinants of fracture healing. Due to availability of experimental data for validation and

Model validation

The axial IFMs predicted by the model were in good agreement with the in-vivo IFMs reported by Claes et al. (1997). As shown in Fig. 5a, the IFMs predicted by the model were within the experimental error ranges. The model predictions show that there is no significant change in callus stiffness in the first two weeks, which is evident from the flat profile of the IFM versus healing time curve and large IFMs around 1 mm in the first two weeks. The IFMs began to reduce from the third week and

Conclusions

In the present study, a computational framework was developed to determine the optimal levels of weight-bearing based on angiogenesis and mechano-regulation mediated fracture healing process under ICF. The model takes into account the spatial and temporal changes in vascularization and callus mechanical properties. The following are the major findings of this study:

  • There are time-dependent and subject (i.e. patient) specific optimal levels of weight-bearing which enhances mechano-regulation

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

CRediT author statement

Ganesharajah Ganadhiepan: Conceptualization, Methodology, Formal analysis, Validation, Investigation, Visualization, Writing - Original Draft. Saeed Miramini: Methodology, Data Curation, Formal analysis, Writing - Review & Editing, Investigation. Minoo Patel: Conceptualization, Methodology, Investigation. Priyan Mendis: Conceptualization, Methodology, Investigation. Lihai Zhang: Conceptualization, Methodology, Supervision, Visualization, Writing - Review & Editing.

Declaration of competing interest

None.

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